Data Engineering Lead

Arine is a rapidly growing healthcare technology and clinical services company focused on improving healthcare outcomes through innovative software solutions. The Data Engineering Lead will lead a team of Data Engineers to develop and maintain data pipelines, ensuring high-quality data delivery and project management while mentoring junior engineers and promoting best practices.

Responsibilities:

• Collaborate with the Implementation team and Data Analyst to review and own DE requirement writeups, ensuring they fully address data needs discussed in implementation calls
• Communicate timelines for deliverables to unblock Data Engineers for ETL pipeline development
• Proactively flag risks to timelines or quality to stakeholders and project managers
• Lead backlog grooming for implementation-related tickets
• Ensure team cross-coverage by making sure the DE Lead and assigned Data Engineer understand the full ETL flow, with at least two additional engineers familiar at a high level
• Conduct thorough peer reviews and DE UATs after initial UAT completion
• Design and maintain data transformation pipelines using dbt, including macros, incremental models, and dbt tests
• Work with QA and CSA teams to resolve post-UAT issues and update the DE UAT checklist
• Provide technical guidance and mentorship to junior engineers, and promote best practices and coding standards
• Identify and escalate inefficiencies within and across teams
• Support project management and provide leadership to peers as needed
• Author and support high-quality technical documentation, assisting junior engineers in doing the same
• Collaborate with the DE Manager to report on DE contractor performance issues
• Champion AI-assisted development across the team - establishing norms, workflows, and expectations for using AI coding tools (e.g., Claude Code, Cursor, Copilot) to generate, iterate, and ship production-quality code
• Model the “builder to reviewer” shift - demonstrating how senior engineers direct AI agents to produce full solutions, then apply rigorous review, testing, and judgment to own the output
• Identify opportunities to automate repetitive engineering work using LLMs and AI tooling, including pipeline scaffolding, boilerplate generation, data transformation logic, and documentation

Requirements:

• 6+ years working with data in production environments
• Proven ability to lead a small team (up to 3 engineers)
• Track record of building automated ETL workflows using Python and dbt SQL
• Hands-on proficiency with modern data technologies and comfort leveraging AI coding assistants to accelerate development, improve code quality, and enhance productivity
• Strong skills in data processing, validation, cleaning, and debugging across complex datasets
• Demonstrated success building production-grade dbt pipelines (macros, incremental and Python models, and testing)
• Deep, demonstrable understanding of healthcare and healthcare claims data
• Comfort working with large-scale datasets (10GB+)
• Excellent verbal and written communication skills
• Demonstrated hands-on experience building software with AI coding tools - not just autocomplete, but directing AI agents to generate complete solutions and applying disciplined review and ownership of the output
• A genuine conviction that AI-augmented development is the future of software engineering, paired with the judgment to validate, test, and take accountability for AI-generated code
• Experience or strong interest in integrating LLMs into engineering workflows beyond development assistance - such as automating data quality checks, generating pipeline logic, or surfacing anomalies
• Ability to pass a background check
• Must live in and be eligible to work in the United States
• Familiarity with AWS services such as S3, DynamoDB, Batch, and Step Functions
• Hands-on knowledge of Snowflake
• Strong data modeling skills for reporting and business intelligence solutions